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First, there’s a need for preparing the data, aka data engineering basics. Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, datawrangling, and data preparation.
Finally, Tuesday is the first day of the AI Expo and Demo Hall , where you can connect with our conference partners and check out the latest developments and research from leading tech companies. This will also be the last day to connect with our partners in the AI Expo and Demo Hall.
Primary Coding Language for Machine Learning Likely to the surprise of no one, python by far is the leading programming language for machine learning practitioners. In the first blog, we’re going to discuss the technical side of things, such as what languages and platforms people are using.
As a Python user, I find the {pySpark} library super handy for leveraging Spark’s capacity to speed up data processing in machine learning projects. But here is a problem: While pySpark syntax is straightforward and very easy to follow, it can be readily confused with other common libraries for datawrangling. distinct().count()
Mini-Bootcamp and VIP Pass holders will have access to four live virtual sessions on data science fundamentals. Confirmed sessions include: An Introduction to DataWrangling with SQL with Sheamus McGovern, Software Architect, Data Engineer, and AI expert Programming with Data: Python and Pandas with Daniel Gerlanc, Sr.
Pre-Bootcamp On-Demand Training Before the conference, you’ll have access to on-demand, self-paced training on core skills like Python, SQL, and more from some of our acclaimed instructors. Day 1 will focus on introducing fundamental data science and AI skills.
For budding data scientists and data analysts, there are mountains of information about why you should learn R over Python and the other way around. Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL.
You’ll also have the chance to learn about the tradeoffs of building AI from scratch or buying it from a third party at the AI Expo and Demo Hall, where Microsoft, neo4j, HPCC, and many more will be showcasing their products and services.
Virtual AI Expo Visit the AI Expo and Demo Hall to connect one-on-one with industry leaders in MLOps, NLP, Machine Learning, and much more. Primer courses include Data Primer SQL Primer Programming Primer with Python AI Primer DataWrangling with Python LLMs, Gen AI, and Prompt Engineering Register for free here!
Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. DataWrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.
At ODSC East 2023 , there will be a number of sessions as part of the machine & deep learning track that will cover the tools, strategies, platforms, and use cases you need to know to excel in the field.
Without the ability to utilize data, create models, visualizations, algorithms, or anything else, you’re left without a story. But it’s not only the ability to work with data, it’s also about scaling your own abilities. Get your ODSC East 2023 Bootcamp ticket while tickets are 50% off!
For a more realistic representation of the UK’s transmission network, I use a simple Python dictionary to define connections between the grid supply points (GSPs) and the transmission lines. Detangling data with layouts We could easily visualize our network on a map using KeyLines’ map mode , but I chose not to use it for this project.
Example template for an exploratory notebook | Source: Author How to organize code in Jupyter notebook For exploratory tasks, the code to produce SQL queries, pandas datawrangling, or create plots is not important for readers. If a reviewer wants more detail, they can always look at the Python module directly.
These Python virtual environments encapsulate and manage Python dependencies, while Docker encapsulates the project’s dependency stack down to the host OS. These Python virtual environments encapsulate and manage Python dependencies. Prerequisite Python 3.8 matplotlib is for data visualization. Flask==2.1.2
Tuesday is the first day of the AI Expo and Demo Hall , where you can connect with our conference partners and check out the latest developments and research from leading tech companies. Finally, get ready for some All Hallows Eve fun with Halloween Data After Dark , featuring a costume contest, candy, and more. What’s next?
Allen Downey, PhD, Principal Data Scientist at PyMCLabs Allen is the author of several booksincluding Think Python, Think Bayes, and Probably Overthinking Itand a blog about data science and Bayesian statistics. A prolific educator, Julien shares his knowledge through code demos, blogs, and YouTube, making complex AI accessible.
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